Overview

Dataset statistics

Number of variables31
Number of observations372899
Missing cells555
Missing cells (%)< 0.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory442.0 MiB
Average record size in memory1.2 KiB

Variable types

Numeric15
Text7
DateTime2
Categorical7

Alerts

OBJECTID is uniformly distributedUniform
OBJECTID has unique valuesUnique
REPORT_HOUR has 12562 (3.4%) zerosZeros
OCC_HOUR has 25847 (6.9%) zerosZeros
LONG_WGS84 has 5750 (1.5%) zerosZeros
LAT_WGS84 has 5750 (1.5%) zerosZeros

Reproduction

Analysis started2024-03-09 16:25:13.104616
Analysis finished2024-03-09 16:27:16.899034
Duration2 minutes and 3.79 seconds
Software versionydata-profiling vv4.6.5
Download configurationconfig.json

Variables

X
Real number (ℝ)

Distinct19049
Distinct (%)5.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-8702251.7
Minimum-8865400.5
Maximum6.32778 × 10-9
Zeros0
Zeros (%)0.0%
Negative367149
Negative (%)98.5%
Memory size2.8 MiB
2024-03-09T16:27:17.188822image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum-8865400.5
5-th percentile-8857425.7
Q1-8846962.3
median-8838090.6
Q3-8829992.6
95-th percentile-8817203.6
Maximum6.32778 × 10-9
Range8865400.5
Interquartile range (IQR)16969.7

Descriptive statistics

Standard deviation1089102.7
Coefficient of variation (CV)-0.12515183
Kurtosis59.854663
Mean-8702251.7
Median Absolute Deviation (MAD)8482.7671
Skewness7.8642927
Sum-3.245061 × 1012
Variance1.1861448 × 1012
MonotonicityNot monotonic
2024-03-09T16:27:17.654587image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6.32778 × 10-95750
 
1.5%
-8851659.93 1731
 
0.5%
-8836642.808 1248
 
0.3%
-8835539.621 1046
 
0.3%
-8822541.767 927
 
0.2%
-8835439.022 861
 
0.2%
-8844478.951 845
 
0.2%
-8836885.407 818
 
0.2%
-8836536.308 794
 
0.2%
-8832824.78 742
 
0.2%
Other values (19039) 358137
96.0%
ValueCountFrequency (%)
-8865400.462 5
 
< 0.1%
-8865097.429 16
< 0.1%
-8864952.938 31
< 0.1%
-8864643.927 24
< 0.1%
-8864367.844 20
< 0.1%
-8864281.237 12
 
< 0.1%
-8864257.448 1
 
< 0.1%
-8864253.24 2
 
< 0.1%
-8864218.911 12
 
< 0.1%
-8864155.335 2
 
< 0.1%
ValueCountFrequency (%)
6.32778 × 10-95750
1.5%
-8807825.644 15
 
< 0.1%
-8807942.195 2
 
< 0.1%
-8808009.7 5
 
< 0.1%
-8808088.381 5
 
< 0.1%
-8808142.037 2
 
< 0.1%
-8808272.927 3
 
< 0.1%
-8808330.222 18
 
< 0.1%
-8808360.446 1
 
< 0.1%
-8808398.583 1
 
< 0.1%

Y
Real number (ℝ)

Distinct19048
Distinct (%)5.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5336600.6
Minimum5.664924 × 10-9
Maximum5442747
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.8 MiB
2024-03-09T16:27:18.061384image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum5.664924 × 10-9
5-th percentile5408411
Q15412926.4
median5419022.1
Q35426947.4
95-th percentile5433698.9
Maximum5442747
Range5442747
Interquartile range (IQR)14021

Descriptive statistics

Standard deviation667897.17
Coefficient of variation (CV)0.12515405
Kurtosis59.850214
Mean5336600.6
Median Absolute Deviation (MAD)6619.4754
Skewness-7.8638611
Sum1.990013 × 1012
Variance4.4608663 × 1011
MonotonicityNot monotonic
2024-03-09T16:27:18.614736image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5.664924 × 10-95750
 
1.5%
5405605.583 1731
 
0.5%
5412410.779 1248
 
0.3%
5412714.173 1046
 
0.3%
5430940.946 927
 
0.2%
5412385.869 861
 
0.2%
5422776.594 845
 
0.2%
5413187.102 818
 
0.2%
5412064.284 794
 
0.2%
5419631.291 742
 
0.2%
Other values (19038) 358137
96.0%
ValueCountFrequency (%)
5.664924 × 10-95750
1.5%
5401671.503 1
 
< 0.1%
5401808.668 25
 
< 0.1%
5401812.48 4
 
< 0.1%
5401904.923 8
 
< 0.1%
5401913.115 3
 
< 0.1%
5401967.921 1
 
< 0.1%
5401988.3 9
 
< 0.1%
5401992.404 5
 
< 0.1%
5402006.144 14
 
< 0.1%
ValueCountFrequency (%)
5442747.005 4
 
< 0.1%
5441795.541 2
 
< 0.1%
5441336.616 9
< 0.1%
5441105.485 1
 
< 0.1%
5440874.142 4
 
< 0.1%
5440705.034 17
< 0.1%
5440697.579 5
 
< 0.1%
5440669.8 2
 
< 0.1%
5440486.742 9
< 0.1%
5440460.552 4
 
< 0.1%

OBJECTID
Real number (ℝ)

UNIFORM  UNIQUE 

Distinct372899
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean186450
Minimum1
Maximum372899
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.8 MiB
2024-03-09T16:27:19.063692image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile18645.9
Q193225.5
median186450
Q3279674.5
95-th percentile354254.1
Maximum372899
Range372898
Interquartile range (IQR)186449

Descriptive statistics

Standard deviation107646.81
Coefficient of variation (CV)0.5773495
Kurtosis-1.2
Mean186450
Median Absolute Deviation (MAD)93225
Skewness5.8118263 × 10-16
Sum6.9527019 × 1010
Variance1.1587836 × 1010
MonotonicityStrictly increasing
2024-03-09T16:27:19.598377image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
< 0.1%
248607 1
 
< 0.1%
248605 1
 
< 0.1%
248604 1
 
< 0.1%
248603 1
 
< 0.1%
248602 1
 
< 0.1%
248601 1
 
< 0.1%
248600 1
 
< 0.1%
248599 1
 
< 0.1%
248598 1
 
< 0.1%
Other values (372889) 372889
> 99.9%
ValueCountFrequency (%)
1 1
< 0.1%
2 1
< 0.1%
3 1
< 0.1%
4 1
< 0.1%
5 1
< 0.1%
6 1
< 0.1%
7 1
< 0.1%
8 1
< 0.1%
9 1
< 0.1%
10 1
< 0.1%
ValueCountFrequency (%)
372899 1
< 0.1%
372898 1
< 0.1%
372897 1
< 0.1%
372896 1
< 0.1%
372895 1
< 0.1%
372894 1
< 0.1%
372893 1
< 0.1%
372892 1
< 0.1%
372891 1
< 0.1%
372890 1
< 0.1%
Distinct324979
Distinct (%)87.1%
Missing0
Missing (%)0.0%
Memory size25.1 MiB
2024-03-09T16:27:20.765121image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length16
Median length14
Mean length13.608122
Min length8

Characters and Unicode

Total characters5074455
Distinct characters13
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique288075 ?
Unique (%)77.3%

Sample

1st rowGO-20141260264
2nd rowGO-20141260033
3rd rowGO-20141259834
4th rowGO-20141264084
5th rowGO-20141260577
ValueCountFrequency (%)
go-20151785704 24
 
< 0.1%
go-201967831 23
 
< 0.1%
go-20231711203 21
 
< 0.1%
go-2023651649 21
 
< 0.1%
go-20222426282 20
 
< 0.1%
go-2015840772 16
 
< 0.1%
go-20222160871 15
 
< 0.1%
go-20231821011 13
 
< 0.1%
go-20231830916 13
 
< 0.1%
go-2019817050 12
 
< 0.1%
Other values (324969) 372721
> 99.9%
2024-03-09T16:27:21.887493image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2 885049
17.4%
0 627112
12.4%
1 625617
12.3%
G 372899
7.3%
O 372899
7.3%
- 372899
7.3%
3 289439
 
5.7%
9 259001
 
5.1%
4 256994
 
5.1%
8 255142
 
5.0%
Other values (3) 757404
14.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 3955758
78.0%
Uppercase Letter 745798
 
14.7%
Dash Punctuation 372899
 
7.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 885049
22.4%
0 627112
15.9%
1 625617
15.8%
3 289439
 
7.3%
9 259001
 
6.5%
4 256994
 
6.5%
8 255142
 
6.4%
7 253591
 
6.4%
5 252879
 
6.4%
6 250934
 
6.3%
Uppercase Letter
ValueCountFrequency (%)
G 372899
50.0%
O 372899
50.0%
Dash Punctuation
ValueCountFrequency (%)
- 372899
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 4328657
85.3%
Latin 745798
 
14.7%

Most frequent character per script

Common
ValueCountFrequency (%)
2 885049
20.4%
0 627112
14.5%
1 625617
14.5%
- 372899
8.6%
3 289439
 
6.7%
9 259001
 
6.0%
4 256994
 
5.9%
8 255142
 
5.9%
7 253591
 
5.9%
5 252879
 
5.8%
Latin
ValueCountFrequency (%)
G 372899
50.0%
O 372899
50.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 5074455
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 885049
17.4%
0 627112
12.4%
1 625617
12.3%
G 372899
7.3%
O 372899
7.3%
- 372899
7.3%
3 289439
 
5.7%
9 259001
 
5.1%
4 256994
 
5.1%
8 255142
 
5.0%
Other values (3) 757404
14.9%
Distinct3652
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size2.8 MiB
Minimum2014-01-01 05:00:00+00:00
Maximum2023-12-31 05:00:00+00:00
2024-03-09T16:27:22.241363image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-09T16:27:22.530538image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
Distinct4130
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Memory size2.8 MiB
Minimum1966-06-09 04:00:00+00:00
Maximum2023-12-31 05:00:00+00:00
2024-03-09T16:27:22.849218image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-09T16:27:23.148034image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

REPORT_YEAR
Real number (ℝ)

Distinct10
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2018.8015
Minimum2014
Maximum2023
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.8 MiB
2024-03-09T16:27:23.423651image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum2014
5-th percentile2014
Q12016
median2019
Q32021
95-th percentile2023
Maximum2023
Range9
Interquartile range (IQR)5

Descriptive statistics

Standard deviation2.9035854
Coefficient of variation (CV)0.0014382719
Kurtosis-1.2201334
Mean2018.8015
Median Absolute Deviation (MAD)3
Skewness-0.099458405
Sum7.5280906 × 108
Variance8.4308082
MonotonicityIncreasing
2024-03-09T16:27:23.634510image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
2023 49395
13.2%
2022 41681
11.2%
2019 40124
10.8%
2018 37372
10.0%
2020 35177
9.4%
2017 35142
9.4%
2021 35132
9.4%
2016 33532
9.0%
2015 32884
8.8%
2014 32460
8.7%
ValueCountFrequency (%)
2014 32460
8.7%
2015 32884
8.8%
2016 33532
9.0%
2017 35142
9.4%
2018 37372
10.0%
2019 40124
10.8%
2020 35177
9.4%
2021 35132
9.4%
2022 41681
11.2%
2023 49395
13.2%
ValueCountFrequency (%)
2023 49395
13.2%
2022 41681
11.2%
2021 35132
9.4%
2020 35177
9.4%
2019 40124
10.8%
2018 37372
10.0%
2017 35142
9.4%
2016 33532
9.0%
2015 32884
8.8%
2014 32460
8.7%

REPORT_MONTH
Categorical

Distinct12
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size22.5 MiB
October
33317 
August
33152 
July
33002 
November
32565 
September
32232 
Other values (7)
208631 

Length

Max length9
Median length7
Mean length6.1413546
Min length3

Characters and Unicode

Total characters2290105
Distinct characters26
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowJanuary
2nd rowJanuary
3rd rowJanuary
4th rowJanuary
5th rowJanuary

Common Values

ValueCountFrequency (%)
October 33317
8.9%
August 33152
8.9%
July 33002
8.9%
November 32565
8.7%
September 32232
8.6%
May 32186
8.6%
June 31705
8.5%
December 30478
8.2%
March 29784
8.0%
April 29304
7.9%
Other values (2) 55174
14.8%

Length

2024-03-09T16:27:23.905441image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
october 33317
8.9%
august 33152
8.9%
july 33002
8.9%
november 32565
8.7%
september 32232
8.6%
may 32186
8.6%
june 31705
8.5%
december 30478
8.2%
march 29784
8.0%
april 29304
7.9%
Other values (2) 55174
14.8%

Most occurring characters

ValueCountFrequency (%)
e 344780
15.1%
r 269352
 
11.8%
u 186185
 
8.1%
b 155090
 
6.8%
a 145820
 
6.4%
y 120362
 
5.3%
t 98701
 
4.3%
m 95275
 
4.2%
c 93579
 
4.1%
J 93383
 
4.1%
Other values (16) 687578
30.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 1917206
83.7%
Uppercase Letter 372899
 
16.3%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 344780
18.0%
r 269352
14.0%
u 186185
9.7%
b 155090
8.1%
a 145820
7.6%
y 120362
 
6.3%
t 98701
 
5.1%
m 95275
 
5.0%
c 93579
 
4.9%
o 65882
 
3.4%
Other values (8) 342180
17.8%
Uppercase Letter
ValueCountFrequency (%)
J 93383
25.0%
A 62456
16.7%
M 61970
16.6%
O 33317
 
8.9%
N 32565
 
8.7%
S 32232
 
8.6%
D 30478
 
8.2%
F 26498
 
7.1%

Most occurring scripts

ValueCountFrequency (%)
Latin 2290105
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 344780
15.1%
r 269352
 
11.8%
u 186185
 
8.1%
b 155090
 
6.8%
a 145820
 
6.4%
y 120362
 
5.3%
t 98701
 
4.3%
m 95275
 
4.2%
c 93579
 
4.1%
J 93383
 
4.1%
Other values (16) 687578
30.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2290105
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 344780
15.1%
r 269352
 
11.8%
u 186185
 
8.1%
b 155090
 
6.8%
a 145820
 
6.4%
y 120362
 
5.3%
t 98701
 
4.3%
m 95275
 
4.2%
c 93579
 
4.1%
J 93383
 
4.1%
Other values (16) 687578
30.0%

REPORT_DAY
Real number (ℝ)

Distinct31
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15.748146
Minimum1
Maximum31
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.8 MiB
2024-03-09T16:27:24.153040image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q18
median16
Q323
95-th percentile29
Maximum31
Range30
Interquartile range (IQR)15

Descriptive statistics

Standard deviation8.7690536
Coefficient of variation (CV)0.55683085
Kurtosis-1.1827777
Mean15.748146
Median Absolute Deviation (MAD)8
Skewness0.0015022035
Sum5872468
Variance76.896301
MonotonicityNot monotonic
2024-03-09T16:27:24.410309image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
18 12679
 
3.4%
17 12606
 
3.4%
22 12539
 
3.4%
20 12489
 
3.3%
19 12434
 
3.3%
23 12421
 
3.3%
16 12417
 
3.3%
11 12331
 
3.3%
1 12329
 
3.3%
28 12323
 
3.3%
Other values (21) 248331
66.6%
ValueCountFrequency (%)
1 12329
3.3%
2 11987
3.2%
3 12134
3.3%
4 12092
3.2%
5 11988
3.2%
6 12161
3.3%
7 12247
3.3%
8 12095
3.2%
9 12282
3.3%
10 12162
3.3%
ValueCountFrequency (%)
31 7189
1.9%
30 11110
3.0%
29 11167
3.0%
28 12323
3.3%
27 12232
3.3%
26 11928
3.2%
25 12056
3.2%
24 12289
3.3%
23 12421
3.3%
22 12539
3.4%

REPORT_DOY
Real number (ℝ)

Distinct366
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean187.19402
Minimum1
Maximum366
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.8 MiB
2024-03-09T16:27:24.699414image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile21
Q199
median190
Q3277
95-th percentile346
Maximum366
Range365
Interquartile range (IQR)178

Descriptive statistics

Standard deviation103.73981
Coefficient of variation (CV)0.55418333
Kurtosis-1.1668373
Mean187.19402
Median Absolute Deviation (MAD)89
Skewness-0.060215137
Sum69804463
Variance10761.947
MonotonicityNot monotonic
2024-03-09T16:27:25.019628image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
231 1209
 
0.3%
304 1209
 
0.3%
261 1184
 
0.3%
204 1183
 
0.3%
259 1167
 
0.3%
262 1163
 
0.3%
240 1156
 
0.3%
160 1153
 
0.3%
144 1153
 
0.3%
290 1150
 
0.3%
Other values (356) 361172
96.9%
ValueCountFrequency (%)
1 1142
0.3%
2 823
0.2%
3 864
0.2%
4 902
0.2%
5 893
0.2%
6 927
0.2%
7 919
0.2%
8 939
0.3%
9 923
0.2%
10 927
0.2%
ValueCountFrequency (%)
366 170
 
< 0.1%
365 1010
0.3%
364 932
0.2%
363 929
0.2%
362 927
0.2%
361 866
0.2%
360 747
0.2%
359 765
0.2%
358 904
0.2%
357 977
0.3%

REPORT_DOW
Categorical

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size23.8 MiB
Monday
55045 
Friday
54686 
Tuesday
54002 
Wednesday
53820 
Thursday
53455 
Other values (2)
101891 

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters3728990
Distinct characters18
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowWednesday
2nd rowWednesday
3rd rowWednesday
4th rowWednesday
5th rowWednesday

Common Values

ValueCountFrequency (%)
Monday 55045
14.8%
Friday 54686
14.7%
Tuesday 54002
14.5%
Wednesday 53820
14.4%
Thursday 53455
14.3%
Saturday 51397
13.8%
Sunday 50494
13.5%

Length

2024-03-09T16:27:25.308459image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-09T16:27:25.629692image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
monday 55045
14.8%
friday 54686
14.7%
tuesday 54002
14.5%
wednesday 53820
14.4%
thursday 53455
14.3%
saturday 51397
13.8%
sunday 50494
13.5%

Most occurring characters

ValueCountFrequency (%)
1066430
28.6%
d 426719
11.4%
a 424296
 
11.4%
y 372899
 
10.0%
u 209348
 
5.6%
e 161642
 
4.3%
s 161277
 
4.3%
r 159538
 
4.3%
n 159359
 
4.3%
T 107457
 
2.9%
Other values (8) 480025
12.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 2289661
61.4%
Space Separator 1066430
28.6%
Uppercase Letter 372899
 
10.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
d 426719
18.6%
a 424296
18.5%
y 372899
16.3%
u 209348
9.1%
e 161642
 
7.1%
s 161277
 
7.0%
r 159538
 
7.0%
n 159359
 
7.0%
o 55045
 
2.4%
i 54686
 
2.4%
Other values (2) 104852
 
4.6%
Uppercase Letter
ValueCountFrequency (%)
T 107457
28.8%
S 101891
27.3%
M 55045
14.8%
F 54686
14.7%
W 53820
14.4%
Space Separator
ValueCountFrequency (%)
1066430
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 2662560
71.4%
Common 1066430
28.6%

Most frequent character per script

Latin
ValueCountFrequency (%)
d 426719
16.0%
a 424296
15.9%
y 372899
14.0%
u 209348
7.9%
e 161642
 
6.1%
s 161277
 
6.1%
r 159538
 
6.0%
n 159359
 
6.0%
T 107457
 
4.0%
S 101891
 
3.8%
Other values (7) 378134
14.2%
Common
ValueCountFrequency (%)
1066430
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3728990
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1066430
28.6%
d 426719
11.4%
a 424296
 
11.4%
y 372899
 
10.0%
u 209348
 
5.6%
e 161642
 
4.3%
s 161277
 
4.3%
r 159538
 
4.3%
n 159359
 
4.3%
T 107457
 
2.9%
Other values (8) 480025
12.9%

REPORT_HOUR
Real number (ℝ)

ZEROS 

Distinct24
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12.714494
Minimum0
Maximum23
Zeros12562
Zeros (%)3.4%
Negative0
Negative (%)0.0%
Memory size2.8 MiB
2024-03-09T16:27:25.924069image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q18
median13
Q318
95-th percentile22
Maximum23
Range23
Interquartile range (IQR)10

Descriptive statistics

Standard deviation6.4709604
Coefficient of variation (CV)0.50894362
Kurtosis-0.90339735
Mean12.714494
Median Absolute Deviation (MAD)5
Skewness-0.27548796
Sum4741222
Variance41.873329
MonotonicityNot monotonic
2024-03-09T16:27:26.211158image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
15 19655
 
5.3%
13 19238
 
5.2%
14 19059
 
5.1%
12 19043
 
5.1%
18 18985
 
5.1%
16 18811
 
5.0%
19 18692
 
5.0%
9 18601
 
5.0%
17 18282
 
4.9%
11 18054
 
4.8%
Other values (14) 184479
49.5%
ValueCountFrequency (%)
0 12562
3.4%
1 11680
3.1%
2 11018
3.0%
3 9371
2.5%
4 7950
2.1%
5 6960
 
1.9%
6 10185
2.7%
7 13651
3.7%
8 16420
4.4%
9 18601
5.0%
ValueCountFrequency (%)
23 15167
4.1%
22 15994
4.3%
21 17615
4.7%
20 18017
4.8%
19 18692
5.0%
18 18985
5.1%
17 18282
4.9%
16 18811
5.0%
15 19655
5.3%
14 19059
5.1%

OCC_YEAR
Real number (ℝ)

Distinct24
Distinct (%)< 0.1%
Missing111
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean2018.7434
Minimum2000
Maximum2023
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.8 MiB
2024-03-09T16:27:26.451805image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum2000
5-th percentile2014
Q12016
median2019
Q32021
95-th percentile2023
Maximum2023
Range23
Interquartile range (IQR)5

Descriptive statistics

Standard deviation2.940644
Coefficient of variation (CV)0.0014566705
Kurtosis-0.75723702
Mean2018.7434
Median Absolute Deviation (MAD)3
Skewness-0.18300771
Sum7.5256331 × 108
Variance8.6473869
MonotonicityNot monotonic
2024-03-09T16:27:26.706649image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
2023 47833
12.8%
2022 41299
11.1%
2019 40098
10.8%
2018 37545
10.1%
2017 35547
9.5%
2020 35196
9.4%
2021 34777
9.3%
2016 33654
9.0%
2015 32938
8.8%
2014 32477
8.7%
Other values (14) 1424
 
0.4%
ValueCountFrequency (%)
2000 27
 
< 0.1%
2001 22
 
< 0.1%
2002 23
 
< 0.1%
2003 17
 
< 0.1%
2004 31
 
< 0.1%
2005 36
< 0.1%
2006 23
 
< 0.1%
2007 36
< 0.1%
2008 49
< 0.1%
2009 82
< 0.1%
ValueCountFrequency (%)
2023 47833
12.8%
2022 41299
11.1%
2021 34777
9.3%
2020 35196
9.4%
2019 40098
10.8%
2018 37545
10.1%
2017 35547
9.5%
2016 33654
9.0%
2015 32938
8.8%
2014 32477
8.7%

OCC_MONTH
Categorical

Distinct12
Distinct (%)< 0.1%
Missing111
Missing (%)< 0.1%
Memory size22.5 MiB
October
33207 
July
32971 
August
32904 
September
32190 
November
32112 
Other values (7)
209404 

Length

Max length9
Median length7
Mean length6.1424536
Min length3

Characters and Unicode

Total characters2289833
Distinct characters26
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowJanuary
2nd rowDecember
3rd rowJanuary
4th rowDecember
5th rowJanuary

Common Values

ValueCountFrequency (%)
October 33207
8.9%
July 32971
8.8%
August 32904
8.8%
September 32190
8.6%
November 32112
8.6%
June 32085
8.6%
May 32001
8.6%
December 30578
8.2%
January 29824
8.0%
March 29419
7.9%
Other values (2) 55497
14.9%

Length

2024-03-09T16:27:26.998751image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
october 33207
8.9%
july 32971
8.8%
august 32904
8.8%
september 32190
8.6%
november 32112
8.6%
june 32085
8.6%
may 32001
8.6%
december 30578
8.2%
january 29824
8.0%
march 29419
7.9%
Other values (2) 55497
14.9%

Most occurring characters

ValueCountFrequency (%)
e 344205
15.0%
r 269212
 
11.8%
u 187073
 
8.2%
b 154472
 
6.7%
a 147453
 
6.4%
y 121181
 
5.3%
t 98301
 
4.3%
m 94880
 
4.1%
J 94880
 
4.1%
c 93204
 
4.1%
Other values (16) 684972
29.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 1917045
83.7%
Uppercase Letter 372788
 
16.3%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 344205
18.0%
r 269212
14.0%
u 187073
9.8%
b 154472
8.1%
a 147453
7.7%
y 121181
 
6.3%
t 98301
 
5.1%
m 94880
 
4.9%
c 93204
 
4.9%
o 65319
 
3.4%
Other values (8) 341745
17.8%
Uppercase Letter
ValueCountFrequency (%)
J 94880
25.5%
A 62016
16.6%
M 61420
16.5%
O 33207
 
8.9%
S 32190
 
8.6%
N 32112
 
8.6%
D 30578
 
8.2%
F 26385
 
7.1%

Most occurring scripts

ValueCountFrequency (%)
Latin 2289833
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 344205
15.0%
r 269212
 
11.8%
u 187073
 
8.2%
b 154472
 
6.7%
a 147453
 
6.4%
y 121181
 
5.3%
t 98301
 
4.3%
m 94880
 
4.1%
J 94880
 
4.1%
c 93204
 
4.1%
Other values (16) 684972
29.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2289833
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 344205
15.0%
r 269212
 
11.8%
u 187073
 
8.2%
b 154472
 
6.7%
a 147453
 
6.4%
y 121181
 
5.3%
t 98301
 
4.3%
m 94880
 
4.1%
J 94880
 
4.1%
c 93204
 
4.1%
Other values (16) 684972
29.9%

OCC_DAY
Real number (ℝ)

Distinct31
Distinct (%)< 0.1%
Missing111
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean15.449821
Minimum1
Maximum31
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.8 MiB
2024-03-09T16:27:27.264359image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q18
median15
Q323
95-th percentile29
Maximum31
Range30
Interquartile range (IQR)15

Descriptive statistics

Standard deviation8.9270578
Coefficient of variation (CV)0.57780978
Kurtosis-1.1913142
Mean15.449821
Median Absolute Deviation (MAD)8
Skewness0.010426992
Sum5759508
Variance79.692361
MonotonicityNot monotonic
2024-03-09T16:27:27.546837image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
1 19894
 
5.3%
18 12522
 
3.4%
20 12412
 
3.3%
15 12364
 
3.3%
17 12317
 
3.3%
16 12190
 
3.3%
12 12140
 
3.3%
19 12124
 
3.3%
24 12104
 
3.2%
14 12057
 
3.2%
Other values (21) 242664
65.1%
ValueCountFrequency (%)
1 19894
5.3%
2 11753
3.2%
3 11783
3.2%
4 11611
3.1%
5 11870
3.2%
6 11874
3.2%
7 11838
3.2%
8 11770
3.2%
9 12048
3.2%
10 11981
3.2%
ValueCountFrequency (%)
31 7270
1.9%
30 10923
2.9%
29 10840
2.9%
28 11911
3.2%
27 11966
3.2%
26 11607
3.1%
25 11918
3.2%
24 12104
3.2%
23 11998
3.2%
22 11959
3.2%

OCC_DOY
Real number (ℝ)

Distinct366
Distinct (%)0.1%
Missing111
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean186.38427
Minimum1
Maximum366
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.8 MiB
2024-03-09T16:27:27.866808image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile19
Q198
median188
Q3276
95-th percentile346
Maximum366
Range365
Interquartile range (IQR)178

Descriptive statistics

Standard deviation104.10708
Coefficient of variation (CV)0.55856153
Kurtosis-1.162468
Mean186.38427
Median Absolute Deviation (MAD)89
Skewness-0.060446032
Sum69481820
Variance10838.285
MonotonicityNot monotonic
2024-03-09T16:27:28.213935image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 2973
 
0.8%
305 1585
 
0.4%
182 1565
 
0.4%
244 1549
 
0.4%
152 1549
 
0.4%
274 1447
 
0.4%
121 1413
 
0.4%
60 1407
 
0.4%
213 1396
 
0.4%
335 1370
 
0.4%
Other values (356) 356534
95.6%
ValueCountFrequency (%)
1 2973
0.8%
2 814
 
0.2%
3 811
 
0.2%
4 845
 
0.2%
5 899
 
0.2%
6 897
 
0.2%
7 900
 
0.2%
8 926
 
0.2%
9 896
 
0.2%
10 915
 
0.2%
ValueCountFrequency (%)
366 197
 
0.1%
365 981
0.3%
364 865
0.2%
363 888
0.2%
362 861
0.2%
361 860
0.2%
360 793
0.2%
359 875
0.2%
358 997
0.3%
357 1002
0.3%

OCC_DOW
Categorical

Distinct7
Distinct (%)< 0.1%
Missing111
Missing (%)< 0.1%
Memory size23.8 MiB
Friday
56230 
Saturday
54923 
Sunday
53046 
Thursday
52779 
Wednesday
52504 
Other values (2)
103306 

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters3727880
Distinct characters18
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowWednesday
2nd rowTuesday
3rd rowWednesday
4th rowTuesday
5th rowWednesday

Common Values

ValueCountFrequency (%)
Friday 56230
15.1%
Saturday 54923
14.7%
Sunday 53046
14.2%
Thursday 52779
14.2%
Wednesday 52504
14.1%
Monday 51866
13.9%
Tuesday 51440
13.8%
(Missing) 111
 
< 0.1%

Length

2024-03-09T16:27:28.534818image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-09T16:27:28.848724image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
friday 56230
15.1%
saturday 54923
14.7%
sunday 53046
14.2%
thursday 52779
14.2%
wednesday 52504
14.1%
monday 51866
13.9%
tuesday 51440
13.8%

Most occurring characters

ValueCountFrequency (%)
1066796
28.6%
a 427711
11.5%
d 425292
 
11.4%
y 372788
 
10.0%
u 212188
 
5.7%
r 163932
 
4.4%
n 157416
 
4.2%
s 156723
 
4.2%
e 156448
 
4.2%
S 107969
 
2.9%
Other values (8) 480617
12.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 2288296
61.4%
Space Separator 1066796
28.6%
Uppercase Letter 372788
 
10.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 427711
18.7%
d 425292
18.6%
y 372788
16.3%
u 212188
9.3%
r 163932
 
7.2%
n 157416
 
6.9%
s 156723
 
6.8%
e 156448
 
6.8%
i 56230
 
2.5%
t 54923
 
2.4%
Other values (2) 104645
 
4.6%
Uppercase Letter
ValueCountFrequency (%)
S 107969
29.0%
T 104219
28.0%
F 56230
15.1%
W 52504
14.1%
M 51866
13.9%
Space Separator
ValueCountFrequency (%)
1066796
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 2661084
71.4%
Common 1066796
28.6%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 427711
16.1%
d 425292
16.0%
y 372788
14.0%
u 212188
8.0%
r 163932
 
6.2%
n 157416
 
5.9%
s 156723
 
5.9%
e 156448
 
5.9%
S 107969
 
4.1%
T 104219
 
3.9%
Other values (7) 376398
14.1%
Common
ValueCountFrequency (%)
1066796
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3727880
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1066796
28.6%
a 427711
11.5%
d 425292
 
11.4%
y 372788
 
10.0%
u 212188
 
5.7%
r 163932
 
4.4%
n 157416
 
4.2%
s 156723
 
4.2%
e 156448
 
4.2%
S 107969
 
2.9%
Other values (8) 480617
12.9%

OCC_HOUR
Real number (ℝ)

ZEROS 

Distinct24
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12.565142
Minimum0
Maximum23
Zeros25847
Zeros (%)6.9%
Negative0
Negative (%)0.0%
Memory size2.8 MiB
2024-03-09T16:27:29.169044image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q17
median14
Q319
95-th percentile23
Maximum23
Range23
Interquartile range (IQR)12

Descriptive statistics

Standard deviation7.273746
Coefficient of variation (CV)0.5788829
Kurtosis-1.1495376
Mean12.565142
Median Absolute Deviation (MAD)6
Skewness-0.33070125
Sum4685529
Variance52.90738
MonotonicityNot monotonic
2024-03-09T16:27:29.411255image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
0 25847
 
6.9%
12 20342
 
5.5%
21 20305
 
5.4%
18 20223
 
5.4%
20 20182
 
5.4%
22 20110
 
5.4%
23 19554
 
5.2%
19 19489
 
5.2%
17 18746
 
5.0%
15 18013
 
4.8%
Other values (14) 170088
45.6%
ValueCountFrequency (%)
0 25847
6.9%
1 15222
4.1%
2 15079
4.0%
3 12462
3.3%
4 9964
 
2.7%
5 7663
 
2.1%
6 6672
 
1.8%
7 7714
 
2.1%
8 10508
2.8%
9 12222
3.3%
ValueCountFrequency (%)
23 19554
5.2%
22 20110
5.4%
21 20305
5.4%
20 20182
5.4%
19 19489
5.2%
18 20223
5.4%
17 18746
5.0%
16 17296
4.6%
15 18013
4.8%
14 15359
4.1%

DIVISION
Categorical

Distinct18
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size21.3 MiB
D51
30937 
D32
29260 
D31
27493 
D14
26085 
D43
25998 
Other values (13)
233126 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1118697
Distinct characters9
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowD43
2nd rowD42
3rd rowD53
4th rowD32
5th rowNSA

Common Values

ValueCountFrequency (%)
D51 30937
 
8.3%
D32 29260
 
7.8%
D31 27493
 
7.4%
D14 26085
 
7.0%
D43 25998
 
7.0%
D41 25958
 
7.0%
D22 24589
 
6.6%
D23 24207
 
6.5%
D55 22888
 
6.1%
D42 22500
 
6.0%
Other values (8) 112984
30.3%

Length

2024-03-09T16:27:29.659752image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
d51 30937
 
8.3%
d32 29260
 
7.8%
d31 27493
 
7.4%
d14 26085
 
7.0%
d43 25998
 
7.0%
d41 25958
 
7.0%
d22 24589
 
6.6%
d23 24207
 
6.5%
d55 22888
 
6.1%
d42 22500
 
6.0%
Other values (8) 112984
30.3%

Most occurring characters

ValueCountFrequency (%)
D 369081
33.0%
3 173299
15.5%
1 169676
15.2%
2 163372
14.6%
5 124928
 
11.2%
4 106887
 
9.6%
N 3818
 
0.3%
S 3818
 
0.3%
A 3818
 
0.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 738162
66.0%
Uppercase Letter 380535
34.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
3 173299
23.5%
1 169676
23.0%
2 163372
22.1%
5 124928
16.9%
4 106887
14.5%
Uppercase Letter
ValueCountFrequency (%)
D 369081
97.0%
N 3818
 
1.0%
S 3818
 
1.0%
A 3818
 
1.0%

Most occurring scripts

ValueCountFrequency (%)
Common 738162
66.0%
Latin 380535
34.0%

Most frequent character per script

Common
ValueCountFrequency (%)
3 173299
23.5%
1 169676
23.0%
2 163372
22.1%
5 124928
16.9%
4 106887
14.5%
Latin
ValueCountFrequency (%)
D 369081
97.0%
N 3818
 
1.0%
S 3818
 
1.0%
A 3818
 
1.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1118697
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
D 369081
33.0%
3 173299
15.5%
1 169676
15.2%
2 163372
14.6%
5 124928
 
11.2%
4 106887
 
9.6%
N 3818
 
0.3%
S 3818
 
0.3%
A 3818
 
0.3%
Distinct54
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size36.2 MiB
2024-03-09T16:27:30.115719image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length70
Median length65
Mean length44.855148
Min length6

Characters and Unicode

Total characters16726440
Distinct characters55
Distinct categories7 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowGo Train
2nd rowSingle Home, House (Attach Garage, Cottage, Mobile)
3rd rowBar / Restaurant
4th rowBar / Restaurant
5th rowStreets, Roads, Highways (Bicycle Path, Private Road)
ValueCountFrequency (%)
house 157039
 
7.0%
apartment 87813
 
3.9%
condo 87813
 
3.9%
rooming 87813
 
3.9%
commercial 83709
 
3.7%
79212
 
3.5%
garage 71408
 
3.2%
home 69426
 
3.1%
mobile 67618
 
3.0%
single 67618
 
3.0%
Other values (135) 1394751
61.9%
2024-03-09T16:27:30.962691image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1881321
 
11.2%
o 1404577
 
8.4%
e 1375828
 
8.2%
a 1086712
 
6.5%
t 1069028
 
6.4%
r 925148
 
5.5%
i 745605
 
4.5%
, 656697
 
3.9%
s 579054
 
3.5%
n 536498
 
3.2%
Other values (45) 6465972
38.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 11162861
66.7%
Uppercase Letter 2219507
 
13.3%
Space Separator 1881321
 
11.2%
Other Punctuation 817396
 
4.9%
Open Punctuation 323417
 
1.9%
Close Punctuation 280477
 
1.7%
Dash Punctuation 41461
 
0.2%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o 1404577
12.6%
e 1375828
12.3%
a 1086712
9.7%
t 1069028
9.6%
r 925148
 
8.3%
i 745605
 
6.7%
s 579054
 
5.2%
n 536498
 
4.8%
m 490567
 
4.4%
l 484369
 
4.3%
Other values (14) 2465475
22.1%
Uppercase Letter
ValueCountFrequency (%)
C 382624
17.2%
H 297405
13.4%
P 266073
12.0%
R 231563
10.4%
A 211015
9.5%
S 187446
8.4%
B 130805
 
5.9%
O 90671
 
4.1%
M 89440
 
4.0%
G 78971
 
3.6%
Other values (12) 253494
11.4%
Other Punctuation
ValueCountFrequency (%)
, 656697
80.3%
. 78449
 
9.6%
/ 75100
 
9.2%
& 4112
 
0.5%
' 3038
 
0.4%
Space Separator
ValueCountFrequency (%)
1881321
100.0%
Open Punctuation
ValueCountFrequency (%)
( 323417
100.0%
Close Punctuation
ValueCountFrequency (%)
) 280477
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 41461
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 13382368
80.0%
Common 3344072
 
20.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
o 1404577
 
10.5%
e 1375828
 
10.3%
a 1086712
 
8.1%
t 1069028
 
8.0%
r 925148
 
6.9%
i 745605
 
5.6%
s 579054
 
4.3%
n 536498
 
4.0%
m 490567
 
3.7%
l 484369
 
3.6%
Other values (36) 4684982
35.0%
Common
ValueCountFrequency (%)
1881321
56.3%
, 656697
 
19.6%
( 323417
 
9.7%
) 280477
 
8.4%
. 78449
 
2.3%
/ 75100
 
2.2%
- 41461
 
1.2%
& 4112
 
0.1%
' 3038
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 16726440
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1881321
 
11.2%
o 1404577
 
8.4%
e 1375828
 
8.2%
a 1086712
 
6.5%
t 1069028
 
6.4%
r 925148
 
5.5%
i 745605
 
4.5%
, 656697
 
3.9%
s 579054
 
3.5%
n 536498
 
3.2%
Other values (45) 6465972
38.7%

PREMISES_TYPE
Categorical

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size23.0 MiB
Outside
100335 
Apartment
87813 
Commercial
74310 
House
67618 
Other
22257 
Other values (2)
20566 

Length

Max length11
Median length10
Mean length7.6876446
Min length5

Characters and Unicode

Total characters2866715
Distinct characters21
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowTransit
2nd rowHouse
3rd rowCommercial
4th rowCommercial
5th rowOutside

Common Values

ValueCountFrequency (%)
Outside 100335
26.9%
Apartment 87813
23.5%
Commercial 74310
19.9%
House 67618
18.1%
Other 22257
 
6.0%
Transit 11162
 
3.0%
Educational 9404
 
2.5%

Length

2024-03-09T16:27:31.472852image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-09T16:27:31.930713image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
outside 100335
26.9%
apartment 87813
23.5%
commercial 74310
19.9%
house 67618
18.1%
other 22257
 
6.0%
transit 11162
 
3.0%
educational 9404
 
2.5%

Most occurring characters

ValueCountFrequency (%)
e 352333
12.3%
t 318784
11.1%
m 236433
 
8.2%
r 195542
 
6.8%
i 195211
 
6.8%
a 192093
 
6.7%
s 179115
 
6.2%
u 177357
 
6.2%
o 151332
 
5.3%
O 122592
 
4.3%
Other values (11) 745923
26.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 2493816
87.0%
Uppercase Letter 372899
 
13.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 352333
14.1%
t 318784
12.8%
m 236433
9.5%
r 195542
7.8%
i 195211
7.8%
a 192093
7.7%
s 179115
7.2%
u 177357
7.1%
o 151332
 
6.1%
d 109739
 
4.4%
Other values (5) 385877
15.5%
Uppercase Letter
ValueCountFrequency (%)
O 122592
32.9%
A 87813
23.5%
C 74310
19.9%
H 67618
18.1%
T 11162
 
3.0%
E 9404
 
2.5%

Most occurring scripts

ValueCountFrequency (%)
Latin 2866715
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 352333
12.3%
t 318784
11.1%
m 236433
 
8.2%
r 195542
 
6.8%
i 195211
 
6.8%
a 192093
 
6.7%
s 179115
 
6.2%
u 177357
 
6.2%
o 151332
 
5.3%
O 122592
 
4.3%
Other values (11) 745923
26.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2866715
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 352333
12.3%
t 318784
11.1%
m 236433
 
8.2%
r 195542
 
6.8%
i 195211
 
6.8%
a 192093
 
6.7%
s 179115
 
6.2%
u 177357
 
6.2%
o 151332
 
5.3%
O 122592
 
4.3%
Other values (11) 745923
26.0%

UCR_CODE
Real number (ℝ)

Distinct22
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1710.2809
Minimum1410
Maximum2135
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.8 MiB
2024-03-09T16:27:32.401432image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1410
5-th percentile1420
Q11430
median1457
Q32120
95-th percentile2135
Maximum2135
Range725
Interquartile range (IQR)690

Descriptive statistics

Standard deviation329.15942
Coefficient of variation (CV)0.19245928
Kurtosis-1.7438895
Mean1710.2809
Median Absolute Deviation (MAD)37
Skewness0.43854082
Sum6.3776202 × 108
Variance108345.92
MonotonicityNot monotonic
2024-03-09T16:27:32.907223image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%)
1430 135814
36.4%
2120 70119
18.8%
2135 58441
15.7%
1420 42319
 
11.3%
1610 33919
 
9.1%
2130 8794
 
2.4%
1460 6408
 
1.7%
1480 3994
 
1.1%
1450 3993
 
1.1%
2132 3075
 
0.8%
Other values (12) 6023
 
1.6%
ValueCountFrequency (%)
1410 2903
 
0.8%
1420 42319
 
11.3%
1430 135814
36.4%
1440 16
 
< 0.1%
1450 3993
 
1.1%
1455 211
 
0.1%
1457 1350
 
0.4%
1460 6408
 
1.7%
1461 741
 
0.2%
1462 28
 
< 0.1%
ValueCountFrequency (%)
2135 58441
15.7%
2133 614
 
0.2%
2132 3075
 
0.8%
2130 8794
 
2.4%
2125 13
 
< 0.1%
2121 16
 
< 0.1%
2120 70119
18.8%
1611 2
 
< 0.1%
1610 33919
9.1%
1480 3994
 
1.1%

UCR_EXT
Real number (ℝ)

Distinct16
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean147.38701
Minimum100
Maximum230
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.8 MiB
2024-03-09T16:27:33.300101image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum100
5-th percentile100
Q1100
median100
Q3200
95-th percentile210
Maximum230
Range130
Interquartile range (IQR)100

Descriptive statistics

Standard deviation52.252193
Coefficient of variation (CV)0.35452373
Kurtosis-1.8892691
Mean147.38701
Median Absolute Deviation (MAD)0
Skewness0.24983236
Sum54960469
Variance2730.2917
MonotonicityNot monotonic
2024-03-09T16:27:33.770561image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
100 191972
51.5%
200 73268
 
19.6%
210 72843
 
19.5%
220 14427
 
3.9%
110 10758
 
2.9%
180 2524
 
0.7%
120 2353
 
0.6%
130 1290
 
0.3%
140 1211
 
0.3%
150 1124
 
0.3%
Other values (6) 1129
 
0.3%
ValueCountFrequency (%)
100 191972
51.5%
110 10758
 
2.9%
120 2353
 
0.6%
130 1290
 
0.3%
140 1211
 
0.3%
150 1124
 
0.3%
160 268
 
0.1%
170 300
 
0.1%
180 2524
 
0.7%
190 118
 
< 0.1%
ValueCountFrequency (%)
230 122
 
< 0.1%
220 14427
 
3.9%
215 177
 
< 0.1%
211 144
 
< 0.1%
210 72843
19.5%
200 73268
19.6%
190 118
 
< 0.1%
180 2524
 
0.7%
170 300
 
0.1%
160 268
 
0.1%
Distinct51
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size24.8 MiB
2024-03-09T16:27:34.333505image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length30
Median length29
Mean length12.653158
Min length3

Characters and Unicode

Total characters4718350
Distinct characters52
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowAssault
2nd rowAssault
3rd rowAssault With Weapon
4th rowAssault Bodily Harm
5th rowRobbery - Swarming
ValueCountFrequency (%)
assault 191436
23.9%
theft 70924
 
8.9%
b&e 67929
 
8.5%
vehicle 62727
 
7.8%
motor 61516
 
7.7%
of 58450
 
7.3%
with 42198
 
5.3%
weapon 39898
 
5.0%
37394
 
4.7%
robbery 33921
 
4.2%
Other values (79) 133631
16.7%
2024-03-09T16:27:35.504749image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
427125
 
9.1%
s 420424
 
8.9%
t 411651
 
8.7%
e 366930
 
7.8%
l 281279
 
6.0%
a 276620
 
5.9%
o 219356
 
4.6%
u 213268
 
4.5%
A 196575
 
4.2%
h 188974
 
4.0%
Other values (42) 1716148
36.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 3326519
70.5%
Uppercase Letter 843266
 
17.9%
Space Separator 427125
 
9.1%
Other Punctuation 84966
 
1.8%
Dash Punctuation 36474
 
0.8%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
s 420424
12.6%
t 411651
12.4%
e 366930
11.0%
l 281279
8.5%
a 276620
8.3%
o 219356
 
6.6%
u 213268
 
6.4%
h 188974
 
5.7%
i 172311
 
5.2%
r 160308
 
4.8%
Other values (14) 615398
18.5%
Uppercase Letter
ValueCountFrequency (%)
A 196575
23.3%
W 91591
10.9%
B 83523
9.9%
O 82633
9.8%
T 72262
 
8.6%
M 72085
 
8.5%
E 67929
 
8.1%
V 62740
 
7.4%
R 38966
 
4.6%
I 16188
 
1.9%
Other values (12) 58774
 
7.0%
Other Punctuation
ValueCountFrequency (%)
& 67929
79.9%
' 8754
 
10.3%
/ 8254
 
9.7%
: 29
 
< 0.1%
Space Separator
ValueCountFrequency (%)
427125
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 36474
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 4169785
88.4%
Common 548565
 
11.6%

Most frequent character per script

Latin
ValueCountFrequency (%)
s 420424
 
10.1%
t 411651
 
9.9%
e 366930
 
8.8%
l 281279
 
6.7%
a 276620
 
6.6%
o 219356
 
5.3%
u 213268
 
5.1%
A 196575
 
4.7%
h 188974
 
4.5%
i 172311
 
4.1%
Other values (36) 1422397
34.1%
Common
ValueCountFrequency (%)
427125
77.9%
& 67929
 
12.4%
- 36474
 
6.6%
' 8754
 
1.6%
/ 8254
 
1.5%
: 29
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 4718350
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
427125
 
9.1%
s 420424
 
8.9%
t 411651
 
8.7%
e 366930
 
7.8%
l 281279
 
6.0%
a 276620
 
5.9%
o 219356
 
4.6%
u 213268
 
4.5%
A 196575
 
4.2%
h 188974
 
4.0%
Other values (42) 1716148
36.4%

MCI_CATEGORY
Categorical

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size23.5 MiB
Assault
197906 
Break and Enter
70148 
Auto Theft
58441 
Robbery
33921 
Theft Over
 
12483

Length

Max length15
Median length7
Mean length9.0755111
Min length7

Characters and Unicode

Total characters3384249
Distinct characters23
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowAssault
2nd rowAssault
3rd rowAssault
4th rowAssault
5th rowRobbery

Common Values

ValueCountFrequency (%)
Assault 197906
53.1%
Break and Enter 70148
 
18.8%
Auto Theft 58441
 
15.7%
Robbery 33921
 
9.1%
Theft Over 12483
 
3.3%

Length

2024-03-09T16:27:35.850841image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-09T16:27:36.180310image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
assault 197906
33.9%
theft 70924
 
12.1%
break 70148
 
12.0%
and 70148
 
12.0%
enter 70148
 
12.0%
auto 58441
 
10.0%
robbery 33921
 
5.8%
over 12483
 
2.1%

Most occurring characters

ValueCountFrequency (%)
t 397419
11.7%
s 395812
11.7%
a 338202
10.0%
e 257624
 
7.6%
A 256347
 
7.6%
u 256347
 
7.6%
211220
 
6.2%
l 197906
 
5.8%
r 186700
 
5.5%
n 140296
 
4.1%
Other values (13) 746376
22.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 2659058
78.6%
Uppercase Letter 513971
 
15.2%
Space Separator 211220
 
6.2%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
t 397419
14.9%
s 395812
14.9%
a 338202
12.7%
e 257624
9.7%
u 256347
9.6%
l 197906
7.4%
r 186700
7.0%
n 140296
 
5.3%
o 92362
 
3.5%
h 70924
 
2.7%
Other values (6) 325466
12.2%
Uppercase Letter
ValueCountFrequency (%)
A 256347
49.9%
T 70924
 
13.8%
B 70148
 
13.6%
E 70148
 
13.6%
R 33921
 
6.6%
O 12483
 
2.4%
Space Separator
ValueCountFrequency (%)
211220
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 3173029
93.8%
Common 211220
 
6.2%

Most frequent character per script

Latin
ValueCountFrequency (%)
t 397419
12.5%
s 395812
12.5%
a 338202
10.7%
e 257624
 
8.1%
A 256347
 
8.1%
u 256347
 
8.1%
l 197906
 
6.2%
r 186700
 
5.9%
n 140296
 
4.4%
o 92362
 
2.9%
Other values (12) 654014
20.6%
Common
ValueCountFrequency (%)
211220
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3384249
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
t 397419
11.7%
s 395812
11.7%
a 338202
10.0%
e 257624
 
7.6%
A 256347
 
7.6%
u 256347
 
7.6%
211220
 
6.2%
l 197906
 
5.8%
r 186700
 
5.5%
n 140296
 
4.1%
Other values (13) 746376
22.1%
Distinct159
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size21.1 MiB
2024-03-09T16:27:36.753970image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length3
Median length2
Mean length2.4074347
Min length1

Characters and Unicode

Total characters897730
Distinct characters13
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row143
2nd row144
3rd row55
4th row27
5th rowNSA
ValueCountFrequency (%)
1 10335
 
2.8%
73 8641
 
2.3%
168 7768
 
2.1%
170 7314
 
2.0%
27 7269
 
1.9%
164 6961
 
1.9%
78 6549
 
1.8%
nsa 5808
 
1.6%
136 5741
 
1.5%
95 5481
 
1.5%
Other values (149) 301032
80.7%
2024-03-09T16:27:37.648027image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 244712
27.3%
6 92006
 
10.2%
2 85465
 
9.5%
3 76757
 
8.6%
4 73546
 
8.2%
7 73006
 
8.1%
5 72735
 
8.1%
8 60375
 
6.7%
0 52901
 
5.9%
9 48803
 
5.4%
Other values (3) 17424
 
1.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 880306
98.1%
Uppercase Letter 17424
 
1.9%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 244712
27.8%
6 92006
 
10.5%
2 85465
 
9.7%
3 76757
 
8.7%
4 73546
 
8.4%
7 73006
 
8.3%
5 72735
 
8.3%
8 60375
 
6.9%
0 52901
 
6.0%
9 48803
 
5.5%
Uppercase Letter
ValueCountFrequency (%)
N 5808
33.3%
S 5808
33.3%
A 5808
33.3%

Most occurring scripts

ValueCountFrequency (%)
Common 880306
98.1%
Latin 17424
 
1.9%

Most frequent character per script

Common
ValueCountFrequency (%)
1 244712
27.8%
6 92006
 
10.5%
2 85465
 
9.7%
3 76757
 
8.7%
4 73546
 
8.4%
7 73006
 
8.3%
5 72735
 
8.3%
8 60375
 
6.9%
0 52901
 
6.0%
9 48803
 
5.5%
Latin
ValueCountFrequency (%)
N 5808
33.3%
S 5808
33.3%
A 5808
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 897730
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 244712
27.3%
6 92006
 
10.2%
2 85465
 
9.5%
3 76757
 
8.6%
4 73546
 
8.2%
7 73006
 
8.1%
5 72735
 
8.1%
8 60375
 
6.7%
0 52901
 
5.9%
9 48803
 
5.4%
Other values (3) 17424
 
1.9%
Distinct159
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size26.2 MiB
2024-03-09T16:27:38.055142image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length37
Median length28
Mean length16.727591
Min length3

Characters and Unicode

Total characters6237702
Distinct characters52
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowWest Rouge
2nd rowMorningside Heights
3rd rowThorncliffe Park
4th rowYork University Heights
5th rowNSA
ValueCountFrequency (%)
park 36331
 
5.3%
west 36127
 
5.3%
east 24598
 
3.6%
heights 20528
 
3.0%
village 17061
 
2.5%
north 15884
 
2.3%
south 14671
 
2.1%
humber-clairville 10335
 
1.5%
university 9492
 
1.4%
york 9403
 
1.4%
Other values (190) 491105
71.6%
2024-03-09T16:27:38.788061image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 631045
 
10.1%
o 429185
 
6.9%
n 408705
 
6.6%
a 395192
 
6.3%
r 393933
 
6.3%
l 373013
 
6.0%
t 353134
 
5.7%
i 330303
 
5.3%
312636
 
5.0%
s 261507
 
4.2%
Other values (42) 2349049
37.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 4820066
77.3%
Uppercase Letter 902641
 
14.5%
Space Separator 312636
 
5.0%
Dash Punctuation 180986
 
2.9%
Other Punctuation 21373
 
0.3%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
W 87422
 
9.7%
C 79626
 
8.8%
P 74002
 
8.2%
B 66088
 
7.3%
H 65799
 
7.3%
S 65770
 
7.3%
E 55020
 
6.1%
M 52623
 
5.8%
D 37273
 
4.1%
Y 35184
 
3.9%
Other values (14) 283834
31.4%
Lowercase Letter
ValueCountFrequency (%)
e 631045
13.1%
o 429185
 
8.9%
n 408705
 
8.5%
a 395192
 
8.2%
r 393933
 
8.2%
l 373013
 
7.7%
t 353134
 
7.3%
i 330303
 
6.9%
s 261507
 
5.4%
g 137060
 
2.8%
Other values (13) 1106989
23.0%
Other Punctuation
ValueCountFrequency (%)
' 9049
42.3%
. 7948
37.2%
/ 4376
20.5%
Space Separator
ValueCountFrequency (%)
312636
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 180986
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 5722707
91.7%
Common 514995
 
8.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 631045
 
11.0%
o 429185
 
7.5%
n 408705
 
7.1%
a 395192
 
6.9%
r 393933
 
6.9%
l 373013
 
6.5%
t 353134
 
6.2%
i 330303
 
5.8%
s 261507
 
4.6%
g 137060
 
2.4%
Other values (37) 2009630
35.1%
Common
ValueCountFrequency (%)
312636
60.7%
- 180986
35.1%
' 9049
 
1.8%
. 7948
 
1.5%
/ 4376
 
0.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 6237702
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 631045
 
10.1%
o 429185
 
6.9%
n 408705
 
6.6%
a 395192
 
6.3%
r 393933
 
6.3%
l 373013
 
6.0%
t 353134
 
5.7%
i 330303
 
5.3%
312636
 
5.0%
s 261507
 
4.2%
Other values (42) 2349049
37.7%
Distinct141
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size21.1 MiB
2024-03-09T16:27:39.326429image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length3
Median length2
Mean length2.217166
Min length1

Characters and Unicode

Total characters826779
Distinct characters13
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row131
2nd row131
3rd row55
4th row27
5th rowNSA
ValueCountFrequency (%)
77 14112
 
3.8%
75 12567
 
3.4%
1 10304
 
2.8%
76 9589
 
2.6%
73 8557
 
2.3%
26 7376
 
2.0%
27 7322
 
2.0%
78 6797
 
1.8%
137 6408
 
1.7%
14 6382
 
1.7%
Other values (131) 283485
76.0%
2024-03-09T16:27:40.177351image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 186766
22.6%
7 120050
14.5%
2 96947
11.7%
3 89722
10.9%
5 60750
 
7.3%
6 60478
 
7.3%
8 53310
 
6.4%
4 51433
 
6.2%
9 46350
 
5.6%
0 42871
 
5.2%
Other values (3) 18102
 
2.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 808677
97.8%
Uppercase Letter 18102
 
2.2%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 186766
23.1%
7 120050
14.8%
2 96947
12.0%
3 89722
11.1%
5 60750
 
7.5%
6 60478
 
7.5%
8 53310
 
6.6%
4 51433
 
6.4%
9 46350
 
5.7%
0 42871
 
5.3%
Uppercase Letter
ValueCountFrequency (%)
N 6034
33.3%
S 6034
33.3%
A 6034
33.3%

Most occurring scripts

ValueCountFrequency (%)
Common 808677
97.8%
Latin 18102
 
2.2%

Most frequent character per script

Common
ValueCountFrequency (%)
1 186766
23.1%
7 120050
14.8%
2 96947
12.0%
3 89722
11.1%
5 60750
 
7.5%
6 60478
 
7.5%
8 53310
 
6.6%
4 51433
 
6.4%
9 46350
 
5.7%
0 42871
 
5.3%
Latin
ValueCountFrequency (%)
N 6034
33.3%
S 6034
33.3%
A 6034
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 826779
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 186766
22.6%
7 120050
14.5%
2 96947
11.7%
3 89722
10.9%
5 60750
 
7.3%
6 60478
 
7.3%
8 53310
 
6.4%
4 51433
 
6.2%
9 46350
 
5.6%
0 42871
 
5.2%
Other values (3) 18102
 
2.2%
Distinct141
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size28.2 MiB
2024-03-09T16:27:40.608423image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length40
Median length31
Mean length22.267295
Min length3

Characters and Unicode

Total characters8303452
Distinct characters64
Distinct categories8 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowRouge (131)
2nd rowRouge (131)
3rd rowThorncliffe Park (55)
4th rowYork University Heights (27)
5th rowNSA
ValueCountFrequency (%)
west 36627
 
3.4%
park 36372
 
3.4%
corridor 23605
 
2.2%
east 15066
 
1.4%
bay 14756
 
1.4%
communities-the 14112
 
1.3%
waterfront 14112
 
1.3%
island 14112
 
1.3%
77 14112
 
1.3%
heights 13379
 
1.3%
Other values (311) 867262
81.5%
2024-03-09T16:27:41.359253image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
690616
 
8.3%
e 605511
 
7.3%
o 454462
 
5.5%
r 454004
 
5.5%
n 418633
 
5.0%
a 385453
 
4.6%
) 372032
 
4.5%
( 372032
 
4.5%
i 365764
 
4.4%
t 365716
 
4.4%
Other values (54) 3819229
46.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 4929578
59.4%
Uppercase Letter 924410
 
11.1%
Decimal Number 808677
 
9.7%
Space Separator 690616
 
8.3%
Close Punctuation 372032
 
4.5%
Open Punctuation 372032
 
4.5%
Dash Punctuation 185748
 
2.2%
Other Punctuation 20359
 
0.2%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
C 118321
12.8%
W 94370
 
10.2%
S 71507
 
7.7%
B 68006
 
7.4%
P 66457
 
7.2%
H 58097
 
6.3%
M 54852
 
5.9%
D 37438
 
4.0%
E 37076
 
4.0%
T 35346
 
3.8%
Other values (14) 282940
30.6%
Lowercase Letter
ValueCountFrequency (%)
e 605511
12.3%
o 454462
9.2%
r 454004
9.2%
n 418633
 
8.5%
a 385453
 
7.8%
i 365764
 
7.4%
t 365716
 
7.4%
l 329631
 
6.7%
s 266282
 
5.4%
d 158126
 
3.2%
Other values (13) 1125996
22.8%
Decimal Number
ValueCountFrequency (%)
1 186766
23.1%
7 120050
14.8%
2 96947
12.0%
3 89722
11.1%
5 60750
 
7.5%
6 60478
 
7.5%
8 53310
 
6.6%
4 51433
 
6.4%
9 46350
 
5.7%
0 42871
 
5.3%
Other Punctuation
ValueCountFrequency (%)
. 8010
39.3%
' 7897
38.8%
/ 4452
21.9%
Space Separator
ValueCountFrequency (%)
690616
100.0%
Close Punctuation
ValueCountFrequency (%)
) 372032
100.0%
Open Punctuation
ValueCountFrequency (%)
( 372032
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 185748
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 5853988
70.5%
Common 2449464
29.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 605511
 
10.3%
o 454462
 
7.8%
r 454004
 
7.8%
n 418633
 
7.2%
a 385453
 
6.6%
i 365764
 
6.2%
t 365716
 
6.2%
l 329631
 
5.6%
s 266282
 
4.5%
d 158126
 
2.7%
Other values (37) 2050406
35.0%
Common
ValueCountFrequency (%)
690616
28.2%
) 372032
15.2%
( 372032
15.2%
1 186766
 
7.6%
- 185748
 
7.6%
7 120050
 
4.9%
2 96947
 
4.0%
3 89722
 
3.7%
5 60750
 
2.5%
6 60478
 
2.5%
Other values (7) 214323
 
8.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 8303452
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
690616
 
8.3%
e 605511
 
7.3%
o 454462
 
5.5%
r 454004
 
5.5%
n 418633
 
5.0%
a 385453
 
4.6%
) 372032
 
4.5%
( 372032
 
4.5%
i 365764
 
4.4%
t 365716
 
4.4%
Other values (54) 3819229
46.0%

LONG_WGS84
Real number (ℝ)

ZEROS 

Distinct19050
Distinct (%)5.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-78.173657
Minimum-79.639247
Maximum0
Zeros5750
Zeros (%)1.5%
Negative367149
Negative (%)98.5%
Memory size2.8 MiB
2024-03-09T16:27:41.698984image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum-79.639247
5-th percentile-79.567608
Q1-79.473615
median-79.393919
Q3-79.321173
95-th percentile-79.206287
Maximum0
Range79.639247
Interquartile range (IQR)0.1524414

Descriptive statistics

Standard deviation9.7835764
Coefficient of variation (CV)-0.12515183
Kurtosis59.854663
Mean-78.173657
Median Absolute Deviation (MAD)0.076201992
Skewness7.8642927
Sum-29150878
Variance95.718368
MonotonicityNot monotonic
2024-03-09T16:27:42.019617image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 5750
 
1.5%
-79.51581405 1731
 
0.5%
-79.38091295 1248
 
0.3%
-79.37100285 1046
 
0.3%
-79.25424114 927
 
0.2%
-79.37009915 861
 
0.2%
-79.45130622 845
 
0.2%
-79.38309225 818
 
0.2%
-79.37995625 794
 
0.2%
-79.34661502 742
 
0.2%
Other values (19040) 358137
96.0%
ValueCountFrequency (%)
-79.63924735 5
 
< 0.1%
-79.63652515 16
< 0.1%
-79.63522717 31
< 0.1%
-79.63245128 24
< 0.1%
-79.62997118 20
< 0.1%
-79.62919318 12
 
< 0.1%
-79.62897948 1
 
< 0.1%
-79.62894168 2
 
< 0.1%
-79.62863329 12
 
< 0.1%
-79.62806218 2
 
< 0.1%
ValueCountFrequency (%)
0 5750
1.5%
-79.12204396 15
 
< 0.1%
-79.12309096 2
 
< 0.1%
-79.12369736 5
 
< 0.1%
-79.12440417 5
 
< 0.1%
-79.12488617 2
 
< 0.1%
-79.12606197 3
 
< 0.1%
-79.12657666 18
 
< 0.1%
-79.12684817 1
 
< 0.1%
-79.12719075 1
 
< 0.1%

LAT_WGS84
Real number (ℝ)

ZEROS 

Distinct19050
Distinct (%)5.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean43.032817
Minimum0
Maximum43.853164
Zeros5750
Zeros (%)1.5%
Negative0
Negative (%)0.0%
Memory size2.8 MiB
2024-03-09T16:27:42.349919image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile43.630323
Q143.659676
median43.699278
Q343.750727
95-th percentile43.794522
Maximum43.853164
Range43.853164
Interquartile range (IQR)0.091051796

Descriptive statistics

Standard deviation5.3855936
Coefficient of variation (CV)0.12515085
Kurtosis59.856635
Mean43.032817
Median Absolute Deviation (MAD)0.043006274
Skewness-7.8644836
Sum16046894
Variance29.004619
MonotonicityNot monotonic
2024-03-09T16:27:43.337088image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 5750
 
1.5%
43.61207981 1731
 
0.5%
43.6563248 1248
 
0.3%
43.6582966 1046
 
0.3%
43.77663627 927
 
0.2%
43.6561629 861
 
0.2%
43.72365706 845
 
0.2%
43.6613701 818
 
0.2%
43.65407279 794
 
0.2%
43.70323439 742
 
0.2%
Other values (19040) 358137
96.0%
ValueCountFrequency (%)
0 5750
1.5%
43.58648695 1
 
< 0.1%
43.58737945 25
 
< 0.1%
43.58740425 4
 
< 0.1%
43.58800576 8
 
< 0.1%
43.58805906 3
 
< 0.1%
43.58841566 1
 
< 0.1%
43.58854826 9
 
< 0.1%
43.58857496 5
 
< 0.1%
43.58866436 14
 
< 0.1%
ValueCountFrequency (%)
43.85316389 4
 
< 0.1%
43.84700008 2
 
< 0.1%
43.84402682 9
< 0.1%
43.84252933 1
 
< 0.1%
43.84103043 4
 
< 0.1%
43.83993472 17
< 0.1%
43.83988642 5
 
< 0.1%
43.83970643 2
 
< 0.1%
43.83852032 9
< 0.1%
43.83835062 4
 
< 0.1%

Interactions

2024-03-09T16:27:04.000081image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-09T16:25:43.273521image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-09T16:25:48.023212image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-09T16:25:54.053010image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-09T16:25:59.874551image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-09T16:26:04.988621image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-09T16:26:11.784775image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-09T16:26:17.026001image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-09T16:26:23.422954image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-09T16:26:29.101900image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-09T16:26:34.049493image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-09T16:26:40.423916image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-09T16:26:46.001927image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-09T16:26:52.948690image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-09T16:26:58.165008image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-09T16:27:04.447575image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-09T16:25:43.582285image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-09T16:25:48.300225image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
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2024-03-09T16:26:33.597295image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-09T16:26:40.119567image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-09T16:26:45.130766image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-09T16:26:52.599928image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-09T16:26:57.827675image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-09T16:27:03.522767image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Missing values

2024-03-09T16:27:10.513532image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-09T16:27:12.613851image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2024-03-09T16:27:15.825627image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

XYOBJECTIDEVENT_UNIQUE_IDREPORT_DATEOCC_DATEREPORT_YEARREPORT_MONTHREPORT_DAYREPORT_DOYREPORT_DOWREPORT_HOUROCC_YEAROCC_MONTHOCC_DAYOCC_DOYOCC_DOWOCC_HOURDIVISIONLOCATION_TYPEPREMISES_TYPEUCR_CODEUCR_EXTOFFENCEMCI_CATEGORYHOOD_158NEIGHBOURHOOD_158HOOD_140NEIGHBOURHOOD_140LONG_WGS84LAT_WGS84
0-8.809036e+065.431523e+061GO-201412602642014/01/01 05:00:00+002014/01/01 05:00:00+002014January11Wednesday12014.0January1.01.0Wednesday1D43Go TrainTransit1430100AssaultAssault143West Rouge131Rouge (131)-79.13291543.780413
1-8.814320e+065.435514e+062GO-201412600332014/01/01 05:00:00+002013/12/31 05:00:00+002014January11Wednesday22013.0December31.0365.0Tuesday22D42Single Home, House (Attach Garage, Cottage, Mobile)House1430100AssaultAssault144Morningside Heights131Rouge (131)-79.18038743.806289
2-8.832825e+065.419631e+063GO-201412598342014/01/01 05:00:00+002014/01/01 05:00:00+002014January11Wednesday02014.0January1.01.0Wednesday0D53Bar / RestaurantCommercial1420100Assault With WeaponAssault55Thorncliffe Park55Thorncliffe Park (55)-79.34661543.703234
3-8.847292e+065.429042e+064GO-201412640842014/01/01 05:00:00+002013/12/31 05:00:00+002014January11Wednesday222013.0December31.0365.0Tuesday21D32Bar / RestaurantCommercial1420110Assault Bodily HarmAssault27York University Heights27York University Heights (27)-79.47657943.764317
46.327780e-095.664924e-095GO-201412605772014/01/01 05:00:00+002014/01/01 05:00:00+002014January11Wednesday42014.0January1.01.0Wednesday2NSAStreets, Roads, Highways (Bicycle Path, Private Road)Outside1610180Robbery - SwarmingRobberyNSANSANSANSA0.0000000.000000
5-8.840629e+065.412225e+066GO-201412606182014/01/01 05:00:00+002014/01/01 05:00:00+002014January11Wednesday52014.0January1.01.0Wednesday2D14Bar / RestaurantCommercial1430100AssaultAssault81Trinity-Bellwoods81Trinity-Bellwoods (81)-79.41671843.655115
6-8.839460e+065.423396e+067GO-201412607302014/01/01 05:00:00+002014/01/01 05:00:00+002014January11Wednesday32014.0January1.01.0Wednesday3D32Streets, Roads, Highways (Bicycle Path, Private Road)Outside1430100AssaultAssault105Lawrence Park North105Lawrence Park North (105)-79.40622343.727681
7-8.813613e+065.428591e+068GO-201412608312014/01/01 05:00:00+002014/01/01 05:00:00+002014January11Wednesday32014.0January1.01.0Wednesday3D43Single Home, House (Attach Garage, Cottage, Mobile)House1430100AssaultAssault136West Hill136West Hill (136)-79.17403143.761395
8-8.820524e+065.422495e+069GO-201412629142014/01/01 05:00:00+002014/01/01 05:00:00+002014January11Wednesday152014.0January1.01.0Wednesday15D43Streets, Roads, Highways (Bicycle Path, Private Road)Outside2135210Theft Of Motor VehicleAuto Theft123Cliffcrest123Cliffcrest (123)-79.23611943.721827
9-8.828387e+065.424471e+0610GO-201412632172014/01/01 05:00:00+002013/12/31 05:00:00+002014January11Wednesday162013.0December31.0365.0Tuesday17D33Apartment (Rooming House, Condo)Apartment2135210Theft Of Motor VehicleAuto Theft43Victoria Village43Victoria Village (43)-79.30675443.734654
XYOBJECTIDEVENT_UNIQUE_IDREPORT_DATEOCC_DATEREPORT_YEARREPORT_MONTHREPORT_DAYREPORT_DOYREPORT_DOWREPORT_HOUROCC_YEAROCC_MONTHOCC_DAYOCC_DOYOCC_DOWOCC_HOURDIVISIONLOCATION_TYPEPREMISES_TYPEUCR_CODEUCR_EXTOFFENCEMCI_CATEGORYHOOD_158NEIGHBOURHOOD_158HOOD_140NEIGHBOURHOOD_140LONG_WGS84LAT_WGS84
372889-8.824422e+065.429589e+06372890GO-202329882152023/12/31 05:00:00+002023/12/31 05:00:00+002023December31365Sunday72023.0December31.0365.0Sunday2D41Apartment (Rooming House, Condo)Apartment1430100AssaultAssault156Bendale-Glen Andrew127Bendale (127)-79.27113443.767864
372890-8.862119e+065.424209e+06372891GO-202329886052023/12/31 05:00:00+002023/12/31 05:00:00+002023December31365Sunday92023.0December31.0365.0Sunday9D23Single Home, House (Attach Garage, Cottage, Mobile)House1430100AssaultAssault1West Humber-Clairville1West Humber-Clairville (1)-79.60976643.732953
372891-8.840677e+065.414066e+06372892GO-202329929252023/12/31 05:00:00+002023/12/31 05:00:00+002023December31365Sunday222023.0December31.0365.0Sunday22D14Streets, Roads, Highways (Bicycle Path, Private Road)Outside1460100Assault Peace OfficerAssault95Annex95Annex (95)-79.41715043.667080
372892-8.845315e+065.413366e+06372893GO-202329881042023/12/31 05:00:00+002023/12/31 05:00:00+002023December31365Sunday62023.0December31.0365.0Sunday6D11Apartment (Rooming House, Condo)Apartment1430100AssaultAssault90Junction Area90Junction Area (90)-79.45881643.662533
372893-8.849874e+065.425325e+06372894GO-202329898152023/12/31 05:00:00+002023/12/31 05:00:00+002023December31365Sunday132023.0December31.0365.0Sunday13D31Single Home, House (Attach Garage, Cottage, Mobile)House2120210Unlawfully In Dwelling-HouseBreak and Enter25Glenfield-Jane Heights25Glenfield-Jane Heights (25)-79.49976843.740196
372894-8.832825e+065.419631e+06372895GO-202329898382023/12/31 05:00:00+002023/12/31 05:00:00+002023December31365Sunday132023.0December31.0365.0Sunday13D53Apartment (Rooming House, Condo)Apartment1430100AssaultAssault55Thorncliffe Park55Thorncliffe Park (55)-79.34661543.703234
372895-8.838191e+065.415297e+06372896GO-202329897772023/12/31 05:00:00+002023/12/31 05:00:00+002023December31365Sunday122023.0December31.0365.0Sunday12D53Other Commercial / Corporate Places (For Profit, Warehouse, Corp. BldgCommercial2120200B&EBreak and Enter95Annex95Annex (95)-79.39482543.675083
372896-8.851986e+065.429799e+06372897GO-202329904112023/12/31 05:00:00+002023/12/31 05:00:00+002023December31365Sunday142023.0December31.0365.0Sunday14D31Open Areas (Lakes, Parks, Rivers)Outside1610100Robbery With WeaponRobbery24Black Creek24Black Creek (24)-79.51874243.769232
372897-8.832606e+065.429093e+06372898GO-202329904152023/12/31 05:00:00+002023/12/31 05:00:00+002023December31365Sunday162023.0December31.0365.0Sunday14D33Apartment (Rooming House, Condo)Apartment1430100AssaultAssault150Fenside-Parkwoods45Parkwoods-Donalda (45)-79.34465143.764646
372898-8.824422e+065.429589e+06372899GO-202329875712023/12/31 05:00:00+002023/12/31 05:00:00+002023December31365Sunday22023.0December31.0365.0Sunday2D41Apartment (Rooming House, Condo)Apartment1460100Assault Peace OfficerAssault156Bendale-Glen Andrew127Bendale (127)-79.27113443.767864